Data Architect - Data Engineering Lead

UnitedHealth Group•Minnetonka, MN

1d•$112,700 - $193,200•Remote

About The Position

We are seeking a Data Architect / Data Engineering Lead to own the end-to-end architecture and engineering standards for modern data platforms. This role partners closely with product, analytics, and engineering teams to design scalable, secure, and cost-effective data solutions - while remaining hands-on with implementation where it matters most. This position blends data architecture leadership (roadmaps, standards, patterns, governance) with data engineering execution (pipelines, modeling, orchestration, performance optimization), ensuring data is delivered reliably and with high quality.

Requirements

8+ years in data engineering and/or data architecture roles with demonstrated ownership of enterprise-scale data solutions
6+ years of data architecture experience: designing modern data platforms, integration patterns, and scalable data delivery approaches
3+ years of data modeling expertise (dimensional, normalized, canonical models; conceptual/logical/physical modeling)
3+ years of proficiency with Python and SQL; solid engineering discipline (testing, documentation, maintainability)
1+ years of experience with cloud data technologies including Snowflake and/or Databricks (Spark-based engineering)
1+ years of workflow orchestration experience with Apache Airflow; experience with managed Airflow platforms such as Astronomer
1+ years of experience implementing production-grade pipelines (batch and/or streaming), including CI/CD practices, environment promotion, and operational support
1+ years of experience leading technical direction, mentoring engineers, and influencing architecture decisions across teams

Nice To Haves

Bachelor’s degree in Information Technology, Computer Science or related field
dbt experience (modeling and transformation lifecycle management)
Experience with Snowpark framwork
Experience with data governance/metadata tools and practices (catalog, lineage, access policies)
Observability tooling experience (pipeline monitoring, alerting, logging, operational dashboards)
Infrastructure-as-code and containerization (Terraform, Docker, Kubernetes) in support of data platforms
Experience supporting regulated data environments (e.g., healthcare/PII/PHI) and security-by-design patterns

Responsibilities

Define and evolve target-state data architecture, including reference architectures, integration patterns, and platform standards across warehouse/lakehouse ecosystems
Lead architectural decision-making for cloud data platforms (e.g., Snowflake, Databricks) including compute/storage patterns, multi-environment strategies, security boundaries, and cost controls
Create technical roadmaps that align business outcomes with scalable data capabilities (analytics, operational reporting, AI/ML readiness)
Own enterprise and domain data modeling standards (conceptual/logical/physical), including dimensional modeling, canonical models, and patterns for curated datasets
Ensure consistent definitions, metrics alignment, and high-quality, analytics-ready data products
Lead design and build of robust ELT/ETL pipelines using Python and distributed processing (Spark) as needed
Drive performance tuning and optimization across pipelines and data platforms (query patterns, clustering/partitioning, incremental loads, caching strategies)
Establish engineering practices that improve reliability, maintainability, and developer productivity
Design and operationalize workflow orchestration using Apache Airflow, including DAG standards, scheduling patterns, dependency management, retries, SLAs, and backfills
Implement and manage Airflow runtime patterns in managed platforms such as Astronomer (or equivalent), including environment promotion strategies and operational readiness
Define and implement data quality frameworks (validation checks, anomaly detection, reconciliation, data contracts) and operational monitoring
Partner with governance/security stakeholders to ensure compliant data handling, access controls, lineage, metadata, and auditability
Lead/mentor engineers and architects through design reviews, code reviews, and architecture governance forums; raise the bar on engineering excellence
Translate complex technical concepts into clear guidance for stakeholders; drive alignment on tradeoffs, timelines, and expected outcomes